Image noise in radiographs has been classically appreciated as the visual appearance of quantum
mottle. Simple statistical models suggest that low contrast target features are visible when their
size and contrast exceeds the noise fluctuations of background regions. The various contrastdetail phantoms used to demonstrate this will be reviewed. However, quantitative measures of
performance based on alternative choice observations from test patterns have lacked sensitivity
as quality measures and are time consuming. In comparison, the Noise Power Spectrum (NPS) is
easy to measure and provides useful information on low frequency noise as well as the noise
texture. The framework for computing NPS will be summarized as background for the second
part of this course.
Learning Objectives: 1. Review the contrast-detail test patterns that have been used to measure
radiographic quality and understand their limitations, 2. Learn why first order measures of image
noise, the spatial standard deviation, are NOT appropriate as a quality measure, and 3.
Understand the computation framework for computing the NPS.